Research
Research Interest
My research focuses on advancing Structural Health Monitoring (SHM) techniques through innovative statistical pattern recognition-based approaches as well as machine learning methods, including artificial neural networks (ANN) and Graph Neural Networks (GNN). I am also working on responses from structures under dynamic load.
On-going Projects
The finite element approach was employed to create a realistic model of a physical bridge. Because typical finite element software is unable to incorporate Vehicle Bridge Interaction (VBI), the entire model was developed in MATLAB. Acceleration data was collected for both damaged and undamaged states under dynamic bridge loading, taking into account the Vehicle Bridge Interaction (VBI). The damage was simulated by lowering the modulus of elasticity. Statistical analysis was employed in MATLAB to implement anomaly detection strategies. In order to verify the accuracy of the obtained data, acceleration sensors are being utilised to collect experimental data from a scaled laboratory bridge structure.
Sakib, N., & Rana, S. (2023). "Vibration-Based Damage Identification of a Steel Frame Using an Output-Only Algorithm." In Proceedings of the Second International Conference on Advances in Civil Infrastructure and Construction Materials, MIST, Dhaka, Bangladesh (pp. 360)
Abstract: There are various approaches used by different research groups to identify structures and structural changes, and the success of a certain methodology may depend on the context in which it is applied. Therefore, it is crucial to verify promising methodologies by testing them on different structures and damage cases. The objective of this study is to investigate a statistical pattern recognition-based method of Structural Health Monitoring (SHM) using a laboratory structure. Sophisticated finite element models and traditional modal parameters are not used in the implementation of the statistical pattern recognition techniques, as they require significant user interaction. Instead, the statistical approaches presented in this paper is solely based on the signal analysis of the measured vibration data. This makes this approach attractive for the development of an automated health monitoring system. A large-scale laboratory structure was constructed at the Qatar University structures laboratory, and a large dataset of vibration signals was obtained under several structural damage scenarios. This paper suggests a statistical moments-based technique to identify damage using the vibration signals. The method does not require labor-intensive supervised learning, and only acceleration sensor data is required to detect damage. Overall, the proposed approach has the potential to be a cost-effective and efficient solution for SHM of various infrastructures.
Keywords: Structural health monitoring, statistical pattern recognition, vibration signals, laboratory structures, damage identification, automated system, statistical moment analysis
Sakib, N., Rana, S., & Jafar, S.B. (2023). "A Statistical Pattern Recognition for Structural Health Monitoring Using Vibration Signals." In Proceedings of the International Conference on Planning, Architecture, and Civil Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh (pp. 453)
Abstract: Structural health monitoring (SHM) is crucial to detect damage in structures at an early stage, allowing timely maintenance and repair to ensure their safety and longevity. This paper presents a study that investigates the feasibility of using a statistical pattern recognition-based method for SHM using a laboratory structure. The proposed approach relies solely on signal analysis of the measured vibration data, making it cost-effective and attractive for the development of an automated health monitoring system. Unlike traditional SHM methods, the proposed approach does not require labor-intensive tuning, expert knowledge, or extensive training, reducing the time and cost required for SHM. The large-scale laboratory structure at Qatar University provides a unique platform to obtain a large dataset of vibration signals under several structural damage scenarios. The study presents a technique to identify damage using Mahalanobis distance between vibration signals of damaged and undamaged conditions. The proposed approach has the potential to be a practical and efficient solution for SHM in civil, mechanical, and aerospace engineering applications, contributing to the development of reliable and accurate health monitoring systems for structures.
Keywords: Structural health monitoring; Statistical pattern recognition; Vibration analysis; Mahalanobis
distance; Automated monitoring systems.
Design of an ETP for an Industry (2022) | Field Study and AutoCad Drawing |
Conducted a comprehensive analysis of the wastewater characteristics and environmental regulations specific to the industry to design an efficient and compliant Effluent Treatment Plant (ETP).
Developed a detailed engineering design of the ETP, including process selection, equipment sizing, and layout optimization, ensuring the effective removal of pollutants and the production of treated effluent that meets the required discharge standards
A Modern Students and Faculty Sports Center (2019) | Field Study and Power Point |
Proposed a well compact design of six storied building having all necessary facilities
Construction cost, method etc were mentioned.
Model, Analysis and Design of 15 Storied Building (2021) | ETABS 18 |
Building was designed as per BNBC 2020
Tender on Construction of Relocated Police Station (2020) | Power Point and AutoCAD |
Objectives of this tender are
Quality
Integrity
Accountability
Relationships
Model, Analysis and Design of Truss Bridge (2020) | SAP 2000 |
Modeled a three span truss bridge using SAP 2000 and design it considering dead, live and wind load only.
Modeling of Building Components (2019)) | Presentation |
A project on development of miniatures of building components
RC beam was modeled showing all of its components